Infrastructural systems such as power transmission systems are often spread over large and hostile geographical terrain. There has been significant research in monitoring health of such infrastructure through remote sensing techniques to alleviate the cost and effort involved. One popular approach to remote monitoring is through automatic or human-assisted interpretation of optical images collected through Unmanned Aerial Vehicles (UAVs). UAV is generally preferred as a data acquisition platform for its advantages, such as (a) flexibility to make observations from close proximity as well as different perspectives, and (b) possibility of on-demand observation. The various vision-based remote monitoring techniques proposed so far attempt to identify individual faults and exceptions in the system. A typical remote monitoring system gathers several aerial images and attempts to analyze them to identify the potential faults. Identification of a fault generally requires an image from a specific viewpoint and desired resolution. Traditionally, UAVs are planned to operate along such a path that they not only capture images from required viewpoints, but also along interpolated path segments between these viewpoints. The limited battery-life of a UAV is known to be the primary cause limiting the range of its flight. Hence it is necessary to make optimal use of flight time for cost-effective monitoring. Blind, unplanned acquisition and processing of images, results in increased image acquisition and processing loads as well as wasteful UAV maneuvering.